Dhivya Nagasubramanian, VP of AI Transformation and Innovation – Interview Series

Interviews Dhivya Nagasubramanian, VP of AI Transformation and Innovation – Interview Series Published August 3, 2026 By Antoine Tardif, CEO & Founder of Unite.AI Add Unite.AI to your preferred sources on Google Dhivya Nagasubramanian is VP of AI Transformation and Innovation at a major U.S. financial institution, where she leads the design, deployment, and governance of production agentic AI systems. She is the author of Agentic AI for Engineers (Apress/Springer Nature), a practical guide to building autonomous AI systems that can be trusted in production. Since its release, the book has recorded more than 6,000 institutional accesses on SpringerLink, holdings in over 260 libraries worldwide, and adoption across universities. She is a USPTO-granted patent holder in applied machine learning. Her research interests include building applications that are resistant to adversarial jailbreak attacks and contributing to broader industry efforts to develop better models for multicultural safety and security. She is a sought-after expert speaker and panelist across multiple industry and academic conferences.Your career has progressed from enterprise consulting and data science into leading AI transformation within financial services. How did those experiences shape Agentic AI for Engineers, and what recurring gap between AI research and enterprise deployment convinced you that this book needed to be written?I started in 2008 building portfolio accounting and performance measurement systems for banking platforms. One of those projects was a GIPS-compliant engine for calculating time-weighted returns, which financial institutions in more than 80 countries eventually used. That work taught me a lesson that has shaped my whole career. In regulated finance, the most dangerous failure is a wrong number that looks right. A subtly incorrect calculation gets trusted, reported, and acted on for years, because nothing appears broken.I saw the same pattern again later. I found a structural gap in the Markov model of a widely used marketing attribution model. The framework was used by hundreds of thousands of users, and the error had survived for years for one reason: the outputs looked reasonable. When I moved into AI transformation in financial services, I watched agentic systems repeat this pattern with much higher stakes. An agent does not just produce a wrong result. It takes action based on it.The gap that convinced me to write the book is that research and enterprise deployment care about different things. Research measures capability on benchmarks. Enterprises depend on how a system behaves under ambiguity, changing data, and adversarial pressure. Most writing about agents stops at the demo stage. I wrote Agentic AI for Engineers for the engineer who has to put their name on a system that will run with limited supervision inside a regulated institution.The book describes the shift from automation to autonomy as a new engineering mindset. What truly separates an autonomous AI agent from a sophisticated automated workflow, and where are organizations most likely to confuse the two?I give teams a simple test. Can you list every path the system can take before you run it? If yes, you have automation. It may be very sophisticated automa